
Temperature Scaling (TS): [1]} were amongst the first to highlight the miscalibration of modern deep neural networks. They proposed to scale the predicted logits by a scalar temperature to calibrate the models.

Entropy Regularization (ERL): [2]} proposed to add and extra entropy term to the cross-entropy learning objective. Their motivation was to increase the entropy of the final predictions thereby reducing the concentration of the confidence at a single high value.

Label Smoothing (LS): [3]} highlighted the benefit of label smoothing in calibrating deep neural networks.

Mixup (MX): [4]} showed that the data augmentation approach of Mixup also helps in calibrating a model.

Focal Loss (FL): In a recent work [5]}, the authors demonstrated the advantages of using focal loss instead of the standard cross-entropy objective w.r.t calibration of a network.

